Análisis exploratorio
Antonio Álvarez Caballero
11 de mayo de 2016
Columnas
## [1] "Frame" "Minerals"
## [3] "Gas" "Supply"
## [5] "TotalMinerals" "TotalGas"
## [7] "TotalSupply" "GroundUnitValue"
## [9] "BuildingValue" "AirUnitValue"
## [11] "AObservedEnemyGroundUnitValue" "AObservedEnemyBuildingValue"
## [13] "AObservedEnemyAirUnitValue" "EnemyMinerals"
## [15] "EnemyGas" "EnemySupply"
## [17] "EnemyTotalMinerals" "EnemyTotalGas"
## [19] "EnemyTotalSupply" "EnemyGroundUnitValue"
## [21] "EnemyBuildingValue" "EnemyAirUnitValue"
## [23] "BObservedEnemyGroundUnitValue" "BObservedEnemyBuildingValue"
## [25] "BObservedEnemyAirUnitValue" "AObservedResourceValue"
## [27] "BObservedResourceValue" "Winner"
## [1] "El ganador es B"
Buildings
## [1] "Pendiente recta ganador: 0.489469915414255"
## [1] "Pendiente recta perdedor: 0.310893402183382"
Selección de características (No terminado)
## [0] train-error:0.016802
## [1] train-error:0.014987
## [2] train-error:0.013198
## [3] train-error:0.012272
## [4] train-error:0.010910
## [5] train-error:0.010431
## [6] train-error:0.010012
## [7] train-error:0.009730
## [8] train-error:0.009525
## [9] train-error:0.009302
## [10] train-error:0.009161
## [11] train-error:0.009127
## [12] train-error:0.008923
## [13] train-error:0.008678
## [14] train-error:0.008517
## [15] train-error:0.008438
## [16] train-error:0.008306
## [17] train-error:0.008328
## [18] train-error:0.008080
## [19] train-error:0.007861
## [20] train-error:0.007688
## [21] train-error:0.007497
## [22] train-error:0.007334
## [23] train-error:0.007283
## [24] train-error:0.007164
## [25] train-error:0.007094
## [26] train-error:0.007018
## [27] train-error:0.006899
## [28] train-error:0.006836
## [29] train-error:0.006728
## [30] train-error:0.006671
## [31] train-error:0.006633
## [32] train-error:0.006561
## [33] train-error:0.006504
## [34] train-error:0.006482
## [35] train-error:0.006355
## [36] train-error:0.006297
## [37] train-error:0.006194
## [38] train-error:0.006186
## [39] train-error:0.006180
## [40] train-error:0.006106
## [41] train-error:0.006043
## [42] train-error:0.006045
## [43] train-error:0.006023
## [44] train-error:0.005987
## [45] train-error:0.005983
## [46] train-error:0.005981
## [47] train-error:0.005977
## [48] train-error:0.005961
## [49] train-error:0.005955
## [50] train-error:0.005957
## [51] train-error:0.005953
## [52] train-error:0.005955
## [53] train-error:0.005957
## [54] train-error:0.005919
## [55] train-error:0.005919
## [56] train-error:0.005919
## [57] train-error:0.005919
## [58] train-error:0.005921
## [59] train-error:0.005923
## [60] train-error:0.005923
## [61] train-error:0.005919
## [62] train-error:0.005915
## [63] train-error:0.005907
## [64] train-error:0.005897
## [65] train-error:0.005889
## [66] train-error:0.005891
## [67] train-error:0.005889
## [68] train-error:0.005887
## [69] train-error:0.005880
## [70] train-error:0.005870
## [71] train-error:0.005870
## [72] train-error:0.005868
## [73] train-error:0.005866
## [74] train-error:0.005866
## [75] train-error:0.005868
## [76] train-error:0.005868
## [77] train-error:0.005870
## [78] train-error:0.005870
## [79] train-error:0.005870
## [80] train-error:0.005870
## [81] train-error:0.005874
## [82] train-error:0.005874
## [83] train-error:0.005874
## [84] train-error:0.005874
## [85] train-error:0.005874
## [86] train-error:0.005874
## [87] train-error:0.005874
## [88] train-error:0.005874
## [89] train-error:0.005876
## [90] train-error:0.005876
## [91] train-error:0.005822
## [92] train-error:0.005822
## [93] train-error:0.005822
## [94] train-error:0.005820
## [95] train-error:0.005822
## [96] train-error:0.005822
## [97] train-error:0.005822
## [98] train-error:0.005816
## [99] train-error:0.005818
Validación cruzada 5x2
## [0] train-error:0.019852+0.000977 test-error:0.037193+0.001137
## [1] train-error:0.016415+0.001793 test-error:0.033763+0.001267
## [2] train-error:0.014221+0.000986 test-error:0.030842+0.000999
## [3] train-error:0.012947+0.000817 test-error:0.028962+0.000809
## [4] train-error:0.011955+0.000670 test-error:0.026720+0.000869
## [5] train-error:0.011384+0.000715 test-error:0.025895+0.000850
## [6] train-error:0.010888+0.000785 test-error:0.024737+0.000724
## [7] train-error:0.010402+0.000753 test-error:0.023888+0.000693
## [8] train-error:0.010106+0.000761 test-error:0.023272+0.000604
## [9] train-error:0.009890+0.000823 test-error:0.022598+0.000509
## [10] train-error:0.009610+0.000841 test-error:0.022063+0.000492
## [11] train-error:0.009387+0.000887 test-error:0.021568+0.000470
## [12] train-error:0.009060+0.000872 test-error:0.021202+0.000454
## [13] train-error:0.008781+0.000785 test-error:0.020930+0.000424
## [14] train-error:0.008543+0.000707 test-error:0.020708+0.000410
## [15] train-error:0.008340+0.000591 test-error:0.020491+0.000405
## [16] train-error:0.008118+0.000545 test-error:0.020334+0.000254
## [17] train-error:0.007975+0.000519 test-error:0.020240+0.000182
## [18] train-error:0.007725+0.000418 test-error:0.020072+0.000220
## [19] train-error:0.007553+0.000403 test-error:0.019877+0.000166
## [20] train-error:0.007377+0.000368 test-error:0.019757+0.000147
## [21] train-error:0.007189+0.000325 test-error:0.019612+0.000237
## [22] train-error:0.007039+0.000322 test-error:0.019539+0.000216
## [23] train-error:0.006949+0.000287 test-error:0.019439+0.000243
## [24] train-error:0.006856+0.000258 test-error:0.019360+0.000225
## [25] train-error:0.006776+0.000264 test-error:0.019320+0.000175
## [26] train-error:0.006721+0.000256 test-error:0.019273+0.000195
## [27] train-error:0.006639+0.000258 test-error:0.019205+0.000179
## [28] train-error:0.006605+0.000259 test-error:0.019151+0.000125
## [29] train-error:0.006505+0.000234 test-error:0.019157+0.000152
## [30] train-error:0.006463+0.000239 test-error:0.019100+0.000173
## [31] train-error:0.006375+0.000201 test-error:0.019092+0.000199
## [32] train-error:0.006308+0.000221 test-error:0.019016+0.000228
## [33] train-error:0.006278+0.000220 test-error:0.018994+0.000275
## [34] train-error:0.006205+0.000239 test-error:0.018958+0.000219
## [35] train-error:0.006168+0.000240 test-error:0.018845+0.000213
## [36] train-error:0.006143+0.000258 test-error:0.018853+0.000253
## [37] train-error:0.006102+0.000232 test-error:0.018807+0.000286
## [38] train-error:0.006088+0.000229 test-error:0.018827+0.000313
## [39] train-error:0.006086+0.000234 test-error:0.018803+0.000343
## [40] train-error:0.006070+0.000243 test-error:0.018788+0.000333
## [41] train-error:0.006057+0.000245 test-error:0.018760+0.000357
## [42] train-error:0.006032+0.000226 test-error:0.018738+0.000328
## [43] train-error:0.006025+0.000225 test-error:0.018720+0.000327
## [44] train-error:0.005995+0.000224 test-error:0.018698+0.000301
## [45] train-error:0.005973+0.000208 test-error:0.018656+0.000315
## [46] train-error:0.005952+0.000208 test-error:0.018668+0.000334
## [47] train-error:0.005952+0.000213 test-error:0.018672+0.000328
## [48] train-error:0.005938+0.000197 test-error:0.018670+0.000317
## [49] train-error:0.005929+0.000198 test-error:0.018662+0.000343
## [50] train-error:0.005923+0.000203 test-error:0.018644+0.000342
## [51] train-error:0.005915+0.000204 test-error:0.018646+0.000339
## [52] train-error:0.005911+0.000200 test-error:0.018646+0.000324
## [53] train-error:0.005895+0.000203 test-error:0.018632+0.000312
## [54] train-error:0.005897+0.000202 test-error:0.018612+0.000311
## [55] train-error:0.005896+0.000202 test-error:0.018599+0.000319
## [56] train-error:0.005874+0.000203 test-error:0.018563+0.000296
## [57] train-error:0.005846+0.000180 test-error:0.018555+0.000287
## [58] train-error:0.005836+0.000196 test-error:0.018545+0.000268
## [59] train-error:0.005829+0.000192 test-error:0.018545+0.000275
## [60] train-error:0.005810+0.000223 test-error:0.018531+0.000292
## [61] train-error:0.005804+0.000232 test-error:0.018519+0.000281
## [62] train-error:0.005797+0.000222 test-error:0.018517+0.000277
## [63] train-error:0.005794+0.000220 test-error:0.018521+0.000278
## [64] train-error:0.005793+0.000221 test-error:0.018513+0.000279
## [65] train-error:0.005786+0.000217 test-error:0.018513+0.000271
## [66] train-error:0.005785+0.000217 test-error:0.018493+0.000260
## [67] train-error:0.005787+0.000217 test-error:0.018487+0.000263
## [68] train-error:0.005785+0.000212 test-error:0.018468+0.000263
## [69] train-error:0.005786+0.000213 test-error:0.018460+0.000248
## [70] train-error:0.005787+0.000214 test-error:0.018462+0.000254
## [71] train-error:0.005764+0.000256 test-error:0.018462+0.000260
## [72] train-error:0.005762+0.000256 test-error:0.018448+0.000258
## [73] train-error:0.005726+0.000245 test-error:0.018464+0.000258
## [74] train-error:0.005726+0.000245 test-error:0.018462+0.000251
## [75] train-error:0.005727+0.000245 test-error:0.018462+0.000250
## [76] train-error:0.005728+0.000246 test-error:0.018456+0.000254
## [77] train-error:0.005729+0.000246 test-error:0.018450+0.000256
## [78] train-error:0.005689+0.000217 test-error:0.018468+0.000247
## [79] train-error:0.005690+0.000217 test-error:0.018464+0.000242
## [80] train-error:0.005692+0.000217 test-error:0.018468+0.000243
## [81] train-error:0.005691+0.000218 test-error:0.018462+0.000242
## [82] train-error:0.005691+0.000218 test-error:0.018458+0.000240
## [83] train-error:0.005686+0.000211 test-error:0.018456+0.000248
## [84] train-error:0.005684+0.000213 test-error:0.018456+0.000248
## [85] train-error:0.005685+0.000213 test-error:0.018450+0.000247
## [86] train-error:0.005681+0.000211 test-error:0.018458+0.000246
## [87] train-error:0.005680+0.000210 test-error:0.018452+0.000245
## [88] train-error:0.005681+0.000211 test-error:0.018449+0.000242
## [89] train-error:0.005681+0.000211 test-error:0.018453+0.000247
## [90] train-error:0.005679+0.000208 test-error:0.018455+0.000245
## [91] train-error:0.005677+0.000207 test-error:0.018452+0.000244
## [92] train-error:0.005677+0.000207 test-error:0.018454+0.000246
## [93] train-error:0.005676+0.000203 test-error:0.018456+0.000228
## [94] train-error:0.005674+0.000202 test-error:0.018454+0.000226
## [95] train-error:0.005667+0.000202 test-error:0.018459+0.000221
## [96] train-error:0.005666+0.000204 test-error:0.018460+0.000225
## [97] train-error:0.005666+0.000204 test-error:0.018457+0.000222
## [98] train-error:0.005656+0.000206 test-error:0.018460+0.000230
## [99] train-error:0.005651+0.000201 test-error:0.018475+0.000214
## train.error.mean train.error.std test.error.mean test.error.std
## 1: 0.019852 0.000977 0.037193 0.001137
## 2: 0.016415 0.001793 0.033763 0.001267
## 3: 0.014221 0.000986 0.030842 0.000999
## 4: 0.012947 0.000817 0.028962 0.000809
## 5: 0.011955 0.000670 0.026720 0.000869
## 6: 0.011384 0.000715 0.025895 0.000850
## 7: 0.010888 0.000785 0.024737 0.000724
## 8: 0.010402 0.000753 0.023888 0.000693
## 9: 0.010106 0.000761 0.023272 0.000604
## 10: 0.009890 0.000823 0.022598 0.000509
## 11: 0.009610 0.000841 0.022063 0.000492
## 12: 0.009387 0.000887 0.021568 0.000470
## 13: 0.009060 0.000872 0.021202 0.000454
## 14: 0.008781 0.000785 0.020930 0.000424
## 15: 0.008543 0.000707 0.020708 0.000410
## 16: 0.008340 0.000591 0.020491 0.000405
## 17: 0.008118 0.000545 0.020334 0.000254
## 18: 0.007975 0.000519 0.020240 0.000182
## 19: 0.007725 0.000418 0.020072 0.000220
## 20: 0.007553 0.000403 0.019877 0.000166
## 21: 0.007377 0.000368 0.019757 0.000147
## 22: 0.007189 0.000325 0.019612 0.000237
## 23: 0.007039 0.000322 0.019539 0.000216
## 24: 0.006949 0.000287 0.019439 0.000243
## 25: 0.006856 0.000258 0.019360 0.000225
## 26: 0.006776 0.000264 0.019320 0.000175
## 27: 0.006721 0.000256 0.019273 0.000195
## 28: 0.006639 0.000258 0.019205 0.000179
## 29: 0.006605 0.000259 0.019151 0.000125
## 30: 0.006505 0.000234 0.019157 0.000152
## 31: 0.006463 0.000239 0.019100 0.000173
## 32: 0.006375 0.000201 0.019092 0.000199
## 33: 0.006308 0.000221 0.019016 0.000228
## 34: 0.006278 0.000220 0.018994 0.000275
## 35: 0.006205 0.000239 0.018958 0.000219
## 36: 0.006168 0.000240 0.018845 0.000213
## 37: 0.006143 0.000258 0.018853 0.000253
## 38: 0.006102 0.000232 0.018807 0.000286
## 39: 0.006088 0.000229 0.018827 0.000313
## 40: 0.006086 0.000234 0.018803 0.000343
## 41: 0.006070 0.000243 0.018788 0.000333
## 42: 0.006057 0.000245 0.018760 0.000357
## 43: 0.006032 0.000226 0.018738 0.000328
## 44: 0.006025 0.000225 0.018720 0.000327
## 45: 0.005995 0.000224 0.018698 0.000301
## 46: 0.005973 0.000208 0.018656 0.000315
## 47: 0.005952 0.000208 0.018668 0.000334
## 48: 0.005952 0.000213 0.018672 0.000328
## 49: 0.005938 0.000197 0.018670 0.000317
## 50: 0.005929 0.000198 0.018662 0.000343
## 51: 0.005923 0.000203 0.018644 0.000342
## 52: 0.005915 0.000204 0.018646 0.000339
## 53: 0.005911 0.000200 0.018646 0.000324
## 54: 0.005895 0.000203 0.018632 0.000312
## 55: 0.005897 0.000202 0.018612 0.000311
## 56: 0.005896 0.000202 0.018599 0.000319
## 57: 0.005874 0.000203 0.018563 0.000296
## 58: 0.005846 0.000180 0.018555 0.000287
## 59: 0.005836 0.000196 0.018545 0.000268
## 60: 0.005829 0.000192 0.018545 0.000275
## 61: 0.005810 0.000223 0.018531 0.000292
## 62: 0.005804 0.000232 0.018519 0.000281
## 63: 0.005797 0.000222 0.018517 0.000277
## 64: 0.005794 0.000220 0.018521 0.000278
## 65: 0.005793 0.000221 0.018513 0.000279
## 66: 0.005786 0.000217 0.018513 0.000271
## 67: 0.005785 0.000217 0.018493 0.000260
## 68: 0.005787 0.000217 0.018487 0.000263
## 69: 0.005785 0.000212 0.018468 0.000263
## 70: 0.005786 0.000213 0.018460 0.000248
## 71: 0.005787 0.000214 0.018462 0.000254
## 72: 0.005764 0.000256 0.018462 0.000260
## 73: 0.005762 0.000256 0.018448 0.000258
## 74: 0.005726 0.000245 0.018464 0.000258
## 75: 0.005726 0.000245 0.018462 0.000251
## 76: 0.005727 0.000245 0.018462 0.000250
## 77: 0.005728 0.000246 0.018456 0.000254
## 78: 0.005729 0.000246 0.018450 0.000256
## 79: 0.005689 0.000217 0.018468 0.000247
## 80: 0.005690 0.000217 0.018464 0.000242
## 81: 0.005692 0.000217 0.018468 0.000243
## 82: 0.005691 0.000218 0.018462 0.000242
## 83: 0.005691 0.000218 0.018458 0.000240
## 84: 0.005686 0.000211 0.018456 0.000248
## 85: 0.005684 0.000213 0.018456 0.000248
## 86: 0.005685 0.000213 0.018450 0.000247
## 87: 0.005681 0.000211 0.018458 0.000246
## 88: 0.005680 0.000210 0.018452 0.000245
## 89: 0.005681 0.000211 0.018449 0.000242
## 90: 0.005681 0.000211 0.018453 0.000247
## 91: 0.005679 0.000208 0.018455 0.000245
## 92: 0.005677 0.000207 0.018452 0.000244
## 93: 0.005677 0.000207 0.018454 0.000246
## 94: 0.005676 0.000203 0.018456 0.000228
## 95: 0.005674 0.000202 0.018454 0.000226
## 96: 0.005667 0.000202 0.018459 0.000221
## 97: 0.005666 0.000204 0.018460 0.000225
## 98: 0.005666 0.000204 0.018457 0.000222
## 99: 0.005656 0.000206 0.018460 0.000230
## 100: 0.005651 0.000201 0.018475 0.000214
## train.error.mean train.error.std test.error.mean test.error.std
